The overall amount of data is booming like never before that in an unstructured manner. By the end of this decade, it is estimated that we will be having nearly 100’s of zettabytes data and roughly 80% of it unstructured. Unstructured data is nothing but images, audio, text, videos, and so on, and those can not be utilised directly for [model building](https://analyticsindiamag.com/step-by-step-guide-to-building-ml-model-registry/). Nowadays, industries are making an effort to leverage this unstructured data as it can contain a vast amount of information. A huge amount of information available on the internet and taking the right steps on data can result in potential business benefits. By putting the right method to implementation can bring useful insight.
[Web scraping](https://analyticsindiamag.com/puppeteer-web-scraping/), surveys, questionnaires, focus groups, etc., are some of the widely used mechanisms for gathering insightful data. However, web scraping is considered the most reliable and efficient data collection method out of all these methods. Web scraping, also termed as [web data extraction](https://analyticsindiamag.com/mechanicalsoup-web-scraping-custom-dataset-tutorial/), is an automatic method for scraping large data from websites. It processes the HTML of a web page to extract data for manipulation, such as collecting textual data and storing it into some [data frames](https://analyticsindiamag.com/comprehensive-guide-to-pandas-dataframes-with-python-codes/) or in a [database](https://analyticsindiamag.com/10-most-used-databases-by-developers-in-2020/).
Following are the common use case where web scraping is used;
* Gathering real estate listing
* Website change detection
* Tracking online presence
* Data integration
* Review scraping from shopping sites
* Weather monitoring
* Data mining
* Scraping data from email
* .. and many more
To proceed with web scraping, we will proceed with a tool called selenium. It is a powerful web browser automation tool that can simulate operations that we humans like to do over the web. It extends its support to various browsers like Chrome, Internet Explorer, Safari, Edge, Firefox. To scrape data from these browsers, selenium provides a module called WebDriver, which is useful to perform various tasks like automated testing, getting cookies, getting screenshots, and many more. Some common use cases of selenium for web scraping are submitting forms, automated login, adding and deleting data, and handling alert prompt. For more details on selenium, you can follow this [official documentation](https://www.selenium.dev/documentation/en/).
Selenium is a portable framework for testing web applications. It is open-source software released under the Apache License 2.0 that runs on Windows, Linux and macOS. Despite serving its major purpose, Selenium is also used as a web scraping tool. Without delving into the components of Selenium, we shall focus on a single component that is useful for web scraping, WebDriver. Selenium WebDriver provides us with an ability to control a web browser through a programming interface to create and execute test cases.
Let us start by installing selenium and a webdriver. WebDrivers support 7 Programming Languages: Python, Java, C#, Ruby, PHP, .Net and Perl. The examples in this manual are with Python language. There are tutorials available on the internet with other languages.
This is the third part of a 4 part tutorial series on web scraping using Scrapy and Selenium. You can reach part-1 by clicking here and part-2 by clicking here. These two parts dealt with web scraping using Scrapy.
Installing Selenium on any Linux OS is easy. Just execute the following command in a terminal and Selenium would be installed automatically.
pip install selenium
Selenium officially has WebDrivers for 5 Web Browsers. Here, we shall see the installation of WebDriver for two of the most widely used browsers: Chrome and Firefox.
First, we need to download the latest stable version of chromedriver from Chrome’s official site. It would be a zip file. All we need to do is extract it and put it in the executable path.
wget https://chromedriver.storage.googleapis.com/83.0.4103.39/chromedriver_linux64.zip unzip chromedriver_linux64.zip sudo mv chromedriver /usr/local/bin/
Installing geckodriver for Firefox is even simpler since it is maintained by Firefox itself. All we need to do is execute the following line in a terminal and you are ready to play around with selenium and geckodriver.
sudo apt install firefox-geckodriver
There are two examples with increasing levels of complexity. First one would be a simpler webpage opening and typing into textboxes and pressing key(s). This example is to showcase how a webpage can be controlled through Selenium using a program. The second one would be a more complex web scraping example involving mouse scrolling, mouse button clicks and navigating to other pages. The goal here is to make you feel confident to start web scraping with Selenium.
Let us try out a simple automation task using Selenium and chromedriver as our training wheel exercise. For this, we would try to log into a Facebook account and we are not performing any kind of data scraping. I am assuming that you have some knowledge of identifying HTML tags used in a webpage using the browser’s developer tools. The following is a piece of python code that opens up a new Chrome browser, opens the Facebook main page, enters a username, password and clicks Login button.
from selenium import webdriver from selenium.webdriver.common.keys import Keys user_name = "Your E-mail" password = "Your Password" # Creating a chromedriver instance driver = webdriver.Chrome() # For Chrome # driver = webdriver.Firefox() # For Firefox # Opening facebook homepage driver.get("https://www.facebook.com") # Identifying email and password textboxes email = driver.find_element_by_id("email") passwd = driver.find_element_by_id("pass") # Sending user_name and password to corresponding textboxes email.send_keys(user_name) passwd.send_keys(password) # Sending a signal that RETURN key has been pressed passwd.send_keys(Keys.RETURN) # driver.quit()
#web-scraping #selenium #web-scraping-series #python
Web automation and web scraping are quite popular among people out there. That’s mainly because people tend to use web scraping and other similar automation technologies to grab information they want from the internet. The internet can be considered as one of the biggest sources of information. If we can use that wisely, we will be able to scrape lots of important facts. However, it is important for us to use appropriate methodologies to get the most out of web scraping. That’s where proxies come into play.
When you are scraping the internet, you will have to go through lots of information available out there. Going through all the information is never an easy thing to do. You will have to deal with numerous struggles while you are going through the information available. Even if you can use tools to automate the task and overcome struggles, you will still have to invest a lot of time in it.
When you are using proxies, you will be able to crawl through multiple websites faster. This is a reliable method to go ahead with web crawling as well and there is no need to worry too much about the results that you are getting out of it.
Another great thing about proxies is that they will provide you with the chance to mimic that you are from different geographical locations around the world. While keeping that in mind, you will be able to proceed with using the proxy, where you can submit requests that are from different geographical regions. If you are keen to find geographically related information from the internet, you should be using this method. For example, numerous retailers and business owners tend to use this method in order to get a better understanding of local competition and the local customer base that they have.
If you want to try out the benefits that come along with web automation, you can use a free web proxy. You will be able to start experiencing all the amazing benefits that come along with it. Along with that, you will even receive the motivation to take your automation campaigns to the next level.
#automation #web #proxy #web-automation #web-scraping #using-proxies #website-scraping #website-scraping-tools
Process of building machine learning, deep learning or AI applications has several steps. One of them is analysis of the data and finding which parts of it are usable and which are not. We also need to pick machine learning algorithms or neural network architectures that we need to use in order to solve the problem. We might even choose to use reinforcement learning or transfer learning. However, often clients don’t have data that could solve their problem. More often than not, it is our job to get data from the web that is going to be utilized by machine learning algorithm or neural network.
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This is usually the rule when we work on computer vision tasks. Clients rely on your ability to gather the data that is going to feed your VGG, ResNet, or custom Convolutional Neural Network. So, in this article we focus on the step that comes before data analysis and all the fancy algorithms – data scraping, or to be more precise, image scraping. We are going to show three ways to get images from some web site using Python. In this article we cover several topics:
#ai #python #beautiful soup #bs4 #image scraping #scrapy #selenium #software #software craft #software craftsmanship #software development #web crawlers #web image crawling #web scraping
The Beautiful Soup module is used for web scraping in Python. Learn how to use the Beautiful Soup and Requests modules in this tutorial. After watching, you will be able to start scraping the web on your own.
📺 The video in this post was made by freeCodeCamp.org
The origin of the article: https://www.youtube.com/watch?v=87Gx3U0BDlo&list=PLWKjhJtqVAbnqBxcdjVGgT3uVR10bzTEB&index=12
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#web scraping #python #beautiful soup #beautiful soup tutorial #web scraping in python #beautiful soup tutorial - web scraping in python
Web scraping has been used to extract data from websites almost from the time the World Wide Web was born. In the early days, scraping was mainly done on static pages — those with known elements, tags, and data.
More recently, however, advanced technologies in web development have made the task a bit more difficult. In this article, we’ll explore how we might go about scraping data in the case that new technology and other factors prevent standard scraping.
As most websites produce pages meant for human readability rather than automated reading, web scraping mainly consisted of programmatically digesting a web page’s mark-up data (think right-click, View Source), then detecting static patterns in that data that would allow the program to “read” various pieces of information and save it to a file or a database.
Courtesy of the author.
If report data were to be found, often, the data would be accessible by passing either form variables or parameters with the URL. For example:
Python has become one of the most popular web scraping languages due in part to the various web libraries that have been created for it. One popular library, Beautiful Soup, is designed to pull data out of HTML and XML files by allowing searching, navigating, and modifying tags (i.e., the parse tree).
Recently, I had a scraping project that seemed pretty straightforward and I was fully prepared to use traditional scraping to handle it. But as I got further into it, I found obstacles that could not be overcome with traditional methods.
Three main issues prevented me from my standard scraping methods:
#selenium #crawling #scraping-with-python #python #web-scraping